Name: SAM: The Sensitivity of Attribution Methods to Hyperparameters - Dr. Chirag
Start: 2021-02-25T17:00:00+02:00
End: 2021-02-25T18:30:00+02:00

Attribution methods can provide powerful insights into the reasons for a classifier’s decision. Perturbation-based explanation methods often measure the contribution of an input feature to an image classifier's outputs by heuristically removing it via e.g., blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Additionally, a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned.
High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. To this extent, integrating generative models for removing input features can ameliorate existing problems by:
(1) generating more plausible counterfactual samples under the true data distribution;
(2) being more accurate according to three metrics: object localization, deletion, and saliency metrics; and
(3) being more robust to hyperparameter changes.

The talk is based on the paper:
SAM: The Sensitivity of Attribution Methods to Hyperparameters (CVPR 2020)
arxiv: https://arxiv.org/abs/2003.08754
git: https://github.com/anguyen8/sam

Presenter BIO:

Bio: Chirag Agarwal is a postdoctoral research fellow at Harvard University and completed his Ph.D. in electrical and computer engineering from the University of Illinois at Chicago under the joint guidance of Dr. Dan Schonfeld and Dr. Anh Nguyen. Chirag's research has primarily focused on three pillars of performance, robustness, and explanations, which he believes are necessary for deploying machine learning models safely to practical applications. His current works, primarily, focus on different aspects of Trustworthy ML like fairness, robustness, and explainability.

More information about Chirag and his research can be found at http://chirag126.github.io/

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